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Report
Scalable
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Knowledge
Composition
Ontology Interoperation
January 19, 1999
Jan Jannink, Prasenjit Mitra, Srinivasan Pichai,
Danladi Verheijen, Gio Wiederhold
Database Group (Infolab), Stanford University
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Road Map
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• SKC Project Overview
– The Problem
» The Approach
» Issues
» Example: NATO Web
– The Algebra & Its Application
– Conclusion & Future Directions
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The Approach
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• Integration of Knowledge from Multiple Sources
– Preserve the autonomy of sources
– Compose ontologies using the algebra
» Spreads the maintenance cost
» Scales smoothly to more complex inferences
– Reuse existing sources and knowledge for
new applications
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Issues
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– Semantic Mismatch
» mismatch in terms
» automatic discovery and resolution expensive
» difficulty in processing and matching terms
– Incomplete Specifications
» full semantics not specified
– Inconsistent Data
» data from multiple sources inconsistent
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Example: NATO Web
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• URLs
http://www.nato.int/family/countries.htm
http://www.nato.int/php/partners.htm
• Partial Contents
legislature (parliament, house, senate)
government
 state head
prime minister
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Austria
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England
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Finland
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SKC methodology
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• Construct an embedding for a frame-like object
in terms of semistructured data as in the OEM
data model
• A rule language for explicitly resolving semantic
mismatches and for restructured views
• Contexts over semistructured data using the
rules to circumscribe areas of interest (similar
to views over relations)
• Unary and Binary operations on these contexts
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Road Map
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• SKC Project Overview
– The Problem
– The Algebra & Its Application
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Unary Operators
Binary Operators
Rule Primitives
Application: Intersection
– Conclusion & Future Directions
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Unary operators
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• Flatten : Build a glossary of terms from an
ontology
• Circumscribe : Induce a restricted ontology
which is of interest for a specific application.
The articulation rules work only on the
circumscribed ontology.
• Filter : Select the instance objects satisfying a
specific condition
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Binary Operators
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• A knowledge based algebra for contexts.
• Binary operations
– Intersection : Find the common schema and
instances between contexts
– Union : Compose contexts to enrich
information
– Difference : Determine the transform between
contexts
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Rule Primitives
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• Provide articulation primitives for matching
concepts between ontologies and restructuring
objects.
– Match nodes, Add a Child, Merge nodes,
Block nodes etc.
• Extraction rules allow us to create contexts from
information sources
– Create Nodes, Sequence a list
– Create explicit nodes to accommodate implicit
assumptions
– Conversion between instances and schema
items permitted
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Application: Intersection
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• Restructuring of two NATO graphs
– 1: Extract the two labeled graphs from the
NATO web sources
– 2: Match the two graphs to identify
corresponding nodes
– 3: Filter out only matching nodes and
restructure one graph to match the structure
of the other
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Application: Intersection
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• Matching of Nodes
– Content Based Matching
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Construct list of labels describing each node
Preprocess labels (if required, to root words)
Rule-based matching
Type checking
Generate heuristic estimates of extent of match
Accept or reject match based on threshold
– Structure Based Matching
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Road Map
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• SKC Project Overview
– The Problem
– The Algebra & Its Application
– Conclusion & Future Directions
» Future Work
» Summary
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Future Work
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• Estimate maintenance costs to validate our
claims
– n sources of size s ; m articulation agents
– Is n * maint[s] + m * maint[agent]
< maint[n * s]
• Enable inference within the source of contexts
• Proofs on properties of the operators and
rewriting expressions.
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Summary
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• Algebra enables interoperation by
– dealing explicitly with differences using
rulesets
– keeping source domains autonomous
• Assumes domain has a common ontology
– composing domain ontologies requires the
algebra to manage the linkages where
articulation occurs
• Articulation knowledge is distributed
– allows specialists to work independently
– supports multiple intersections and views
• Maintenance is structured and partitioned
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